OAI/NumberTheory/JointDickman/Amplification/CandidateConditionalMean.lean

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A uniform conditional mean for the actual forward candidate entry

  1. theorem OAI.JointDickman.latentCandidateKernel_forward_conditional_mean line 13

    Source documentation

    The other endpoint types may be completely arbitrary. Averaging just the upper endpoint gives the manuscript's uniform bound.

    theorem latentCandidateKernel_forward_conditional_mean
        (hFord : PublishedInputs.FordUpperSieveInput)
        (hM : PublishedInputs.PrimeReciprocalMertensInput) :
        ∃ K : ℝ, 0 < K ∧ ∀ᶠ B : ℕ in atTop, ∀ (L T H M : ℕ) (τ C : ℝ),
          0 < T → (T : ℝ) ≤ Real.exp ((1/10 : ℝ)*B) →
          ∀ (S : Fin M → Finset ℕ) (χ : BlockCandidateIndex M → ℝ),
          (∀ e, χ e ≤ 1) → ∀ i k : Fin M, i < k → S i ⊆ auxiliaryPrimes B →
          finiteExpectation (independentPrimeSetMass B)
            (fun R => latentCandidateKernel B L T H M τ C (Function.update S k R.val) χ i k) ≤
              K/(T : ℝ)*singularFactor 24 (k.val-i.val) := by
    …

    Mentions: OAI.JointDickman.PublishedInputs.FordUpperSieveInput, OAI.JointDickman.PublishedInputs.PrimeReciprocalMertensInput, OAI.JointDickman.BlockCandidateIndex, OAI.JointDickman.auxiliaryPrimes, OAI.JointDickman.finiteExpectation, OAI.JointDickman.independentPrimeSetMass, OAI.JointDickman.latentCandidateKernel, OAI.JointDickman.singularFactor

  2. theorem OAI.JointDickman.latentCandidateKernel_forward_mean line 40

    Source documentation

    The same estimate integrated over the independent endpoint law.

    theorem latentCandidateKernel_forward_mean
        (hFord : PublishedInputs.FordUpperSieveInput)
        (hM : PublishedInputs.PrimeReciprocalMertensInput) :
        ∃ K : ℝ, 0 < K ∧ ∀ᶠ B : ℕ in atTop, ∀ (L T H M : ℕ) (τ C : ℝ),
          0 < T → (T : ℝ) ≤ Real.exp ((1/10 : ℝ)*B) →
          ∀ χ : BlockCandidateIndex M → ℝ, (∀ e, χ e ≤ 1) → ∀ i k : Fin M, i < k →
          finiteExpectation (siteProductMass (fun _ : Fin M => independentPrimeSetMass B))
            (fun S => latentCandidateKernel B L T H M τ C (fun i => (S i).val) χ i k) ≤
              K/(T : ℝ)*singularFactor 24 (k.val-i.val) := by
    …

    Mentions: OAI.JointDickman.PublishedInputs.FordUpperSieveInput, OAI.JointDickman.PublishedInputs.PrimeReciprocalMertensInput, OAI.JointDickman.BlockCandidateIndex, OAI.JointDickman.finiteExpectation, OAI.JointDickman.independentPrimeSetMass, OAI.JointDickman.latentCandidateKernel, OAI.JointDickman.singularFactor